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Ankur Sikarwar

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Aishwarya Agrawal
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Traitement du langage naturel
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning
Qian Yang
Ankur Sikarwar
Huy Le
Le Zhang
Zhuan Shi
Perouz Taslakian
Aishwarya Agrawal
Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained … (voir plus)geometry needed for the task. Thinking with images aims to address this by generating an intermediate thinking image, but recent work shows that models often ignore the visual evidence in these traces. We therefore ask how to make visual thinking matter, and what kind of visual thinking works best. We study these questions in unified multimodal models (UMMs), which natively support interleaved image-text generation. For the first question, we propose View Dropout (VDrop), a training-time intervention that hides parts of one input view from the answer span while keeping them visible to the thinking-image tokens. This encourages the model to use the thinking image when answering, instead of relying only on the input views. Once the thinking image is used for answer prediction, we study which type of visual thinking is most effective. We frame this as a learnability-informativeness tradeoff and compare three thinking-image variants: top-down, panoramic, and point-matching renderings. Trained on synthetic scenes and evaluated on five real-world out-of-domain benchmarks, panoramic visual thinking with VDrop is the only configuration that is both informative and learnable, and it achieves the best out-of-domain generalization.
2026-05-25
arXiv (prépublication)
doi.org
arxiv.org
Communicating about Space: Language-Mediated Spatial Integration Across Partial Views
Ankur Sikarwar
Debangan Mishra
Sudarshan Nikhil
Ponnurangam Kumaraguru
Aishwarya Agrawal
Humans build shared spatial understanding by communicating partial, viewpoint-dependent observations. We ask whether Multimodal Large Langua… (voir plus)ge Models (MLLMs) can do the same, aligning distinct egocentric views through dialogue to form a coherent, allocentric mental model of a shared environment. To study this systematically, we introduce COSMIC, a benchmark for Collaborative Spatial Communication. In this setting, two static MLLM agents observe a 3D indoor environment from different viewpoints and exchange natural-language messages to solve spatial queries. COSMIC contains 899 diverse scenes and 1250 question-answer pairs spanning five tasks. We find a capability hierarchy, MLLMs are most reliable at identifying shared anchor objects across views, perform worse on relational reasoning, and largely fail at building globally consistent maps, performing near chance, even for frontier models. Moreover, we find thinking capability yields gains in anchor grounding, but is insufficient for higher-level spatial communication. To contextualize model behavior, we collect 250 human-human dialogues. Humans achieve 95% aggregate accuracy, while the best model, Gemini-3-Pro-Thinking, reaches 72%, leaving substantial room for improvement. Moreover, human conversations grow more precise as partners align on a shared spatial understanding, whereas MLLMs keep exploring without converging, suggesting limited capacity to form and sustain a robust shared mental model throughout the dialogue. Our code and data is available at https://github.com/ankursikarwar/Cosmic.
2026-03-27
arXiv (prépublication)
doi.org
arxiv.org
The Promise of RL for Autoregressive Image Editing
Saba Ahmadi
Rabiul Awal
Ankur Sikarwar
Amirhossein Kazemnejad
Ge Ya Luo
Juan A. Rodriguez
Sai Rajeswar
Siva Reddy
Christopher Pal
Benno Krojer
Aishwarya Agrawal
While image generation techniques are now capable of producing high-quality images that respect prompts which span multiple sentences, the t… (voir plus)ask of text-guided image editing remains a challenge. Even edit requests that consist of only a few words often fail to be executed correctly. We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learning (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net